Jie Cheng (www.cs.ualberta.ca/~jcheng)

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KDD CUP 2001 Task 1Thrombin

• Jie Cheng (www.cs.ualberta.ca/jcheng)
• Global Analytics
• Canadian Imperial Bank of Commerce

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Overview

• Objective
• Prediction of molecular bioactivity for drug
  design -- binding to Thrombin
• Data
• Training 1909 cases (42 positive), 139,351
  binary features
• Test 634 cases
• Challenge
• Highly imbalanced, high-dimensional, different
  distribution
• My approach
• Bayesian network predictive model

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Bayesian Networks (BN)

• What is a Bayesian Network
• Two ways to view it
• Encodes conditional independent relationships
• Represents the joint probability distribution

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My work related to BN

• Developed an efficient approach to learn BN from
  data (paper to appear in Artificial Intelligence
  Journal)
• BN PowerConstructor system
• available since 1997, thousands of downloads and
  many regular users
• Learning BNs as predictive models
• BN PowerPredictor system
• available since 2000
• Applied BN learning to UCI benchmark datasets,
  Power plant fault diagnosis, Financial risk
  analysis

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Approach to Thrombin data

• Pre-processing Feature subset selection using
  mutual information (200 of 139,351 features)
• Learning Bayesian network models of different
  complexity (2 to 12 features)
• Choosing a model (ROC area, model complexity)
• Cost function?
• From posterior probability only 10 cut points
  30, 31, 32, 71, 72, 74, 75, 215, 223, 550

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The model its performance
Activity
10695
91839
predicted
16794
79651
pos neg
pos 95 55
neg 128 356
20 parameters
Actual
ROC
Accuracy 0.711Weighted Accuracy 0.684
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Bayesian networks make good classifiers!

• Accurate
• UCI datasets
• Efficient
• Learning linear to number of samples, O(N2) to
  the number of features seconds to minutes
• Inference simple multiplications
• Markov blanket for feature selection
• Easy to understand, easy to incorporate domain
  knowledge

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BN PowerPredictor System

• Download
• http//www.cs.ualberta.ca/jcheng/bnsoft.htm
• Features
• Support domain knowledge input
• Support multiple database and spreadsheet formats
• Automatic feature subset selection
• Automatic model selection using wrapper approach
• provide equal size, equal frequency and entropy
  based discretization
• Support cost function definition
• Instant/batch inference models